• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Monday, August 18, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News

Deep learning method that transforms shapes to be presented at SIGGRAPH Asia

Bioengineer by Bioengineer
October 18, 2019
in Science News
Reading Time: 3 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

Turning chairs into tables

IMAGE

Credit: ACM SIGGRAPH Asia 2019


Turning a chair into a table, or vice versa, might sound like somewhat of a magic trick. In this case, zero magic is involved, just plenty of complex geometry and machine learning.

Called LOGAN, the deep neural network, i.e., a machine of sorts, can learn to transform the shapes of two different objects, for example, a chair and a table, in a natural way, without seeing any paired transforms between the shapes. All the machine had seen was a bunch of tables and a bunch of chairs, and it could automatically translate shapes between the two unpaired domains. LOGAN can also automatically perform both content and style transfers between two different types of shapes without any changes to its network architecture.

The team of researchers behind LOGAN, from Simon Fraser University, Shenzhen University, and Tel Aviv University, are set to present their work at ACM SIGGRAPH Asia held Nov. 17 to 20 in Brisbane, Australia. SIGGRAPH Asia, now in its 12th year, attracts the most respected technical and creative people from around the world in computer graphics, animation, interactivity, gaming, and emerging technologies.

“Shape transform is one of the most fundamental and frequently encountered problems in computer graphics and geometric modeling,” says senior coauthor of the work, Hao (Richard) Zhang, professor of computing science at Simon Fraser University. “What is new and emerging is to tie this important problem to deep learning–can a machine learn to transform shapes, particularly under the unsupervised or unpaired setting?”

In this work, the researchers turned to a well-known technique in machine learning, Generative Adversarial Network (GAN), for unpaired general-purpose shape transforms. Their network is trained on two sets of shapes, e.g., tables and chairs or different letters. There is neither a pairing between shapes in the two domains to guide shape translation nor any point-wise correspondence between any shapes. Once trained, the researchers’ method takes a point-set shape from one domain, a table or a chair, and transforms into the other.

LOGAN overcomes a key challenge in shape transform techniques. Given two sets of shapes–chairs and tables–it is challenging for the network to learn which particular shape features should be preserved or altered to result in realistic transformation of the object, from chair to table and vice versa. The team’s method learns the unique differences in features and can automatically determine which features should be kept or discarded in order to achieve the desired shape transform, and can do so without supervision.

Other techniques in computer vision for unpaired image-to-image translation have been developed and have been successful in translating style features, but most have not achieved shape translation. “In 2017, CycleGAN and DualGAN, two highly influential works from computer vision were developed for unpaired image-to-image style translation. LOGAN specifically produces realistic shape translations, both in style and content, for the first time,” notes Zhang. Additionally, the researchers demonstrate that LOGAN can learn “style-preserving” content transfers. For instance, the network can automatically transform a letter ‘R’ into a ‘P’ of the same font style, or with respect to style translation, their method is able to translate a bold-face letter ‘A’ into an italicized ‘A’.

To devise their method, the researchers train a neural network which encodes the two types of input shapes into a common latent space. In deep learning, the latent space is represented by the bottleneck layer where the network captures the features of the
input data. LOGAN is not only trained to turn a chair code to a table code, but also trained to turn a table code to the same table code. The latter enables “feature preservation” and helps maintain certain table features during chair-to-table shape translations.

In ablation studies, the researchers demonstrate LOGAN’s superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches. Their study shows that LOGAN is able to learn what shape features to keep during transforms, and the results accurately resemble the desired object.

In future work, the team aims to fine tune LOGAN to work on all domain pairs to make it truly general-purpose. The current version of LOGAN also is not yet smart enough to understand the meanings of the shapes, and the researchers are working on making the network “smarter” to incorporate this information.

###

Zhang’s collaborators on “LOGAN: Unpaired Shape Transform in Latent Overcomplete Space” include Kangxue Yin (Simon Fraser University); Zhiqin Chen (Simon Fraser University); Hui Huang (Shenzhen University); and Daniel Cohen-Or (Tel Aviv University). For the team’s paper, visit their project page.

Media Contact
Illka Gobius
[email protected]
65-976-98370

Tags: Computer ScienceSoftware EngineeringTechnology/Engineering/Computer Science
Share12Tweet8Share2ShareShareShare2

Related Posts

A Laser-Free Alternative to LASIK: Exploring New Vision Correction Methods

A Laser-Free Alternative to LASIK: Exploring New Vision Correction Methods

August 18, 2025
Novel Small Molecule Shows Promise in Mitigating Acetaminophen-Induced Liver Injury

Novel Small Molecule Shows Promise in Mitigating Acetaminophen-Induced Liver Injury

August 18, 2025

Mapping Key Kinase Mutations in Oral Cancer

August 18, 2025

Fe-Lattice O–O Ligands Boost Water Oxidation Catalysis

August 18, 2025
Please login to join discussion

POPULAR NEWS

  • blank

    Molecules in Focus: Capturing the Timeless Dance of Particles

    141 shares
    Share 56 Tweet 35
  • Neuropsychiatric Risks Linked to COVID-19 Revealed

    80 shares
    Share 32 Tweet 20
  • Modified DASH Diet Reduces Blood Sugar Levels in Adults with Type 2 Diabetes, Clinical Trial Finds

    59 shares
    Share 24 Tweet 15
  • Predicting Colorectal Cancer Using Lifestyle Factors

    47 shares
    Share 19 Tweet 12

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

A Laser-Free Alternative to LASIK: Exploring New Vision Correction Methods

Novel Small Molecule Shows Promise in Mitigating Acetaminophen-Induced Liver Injury

Mapping Key Kinase Mutations in Oral Cancer

  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.